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高忠科

发布时间: 2018-11-13     来源:

个人资料:

姓名高忠科

职称教授 / 博士生导师

学科专业:检测技术与自动化装置

通讯地址:天津大学电气与自动化工程学院26教学楼E

电子信箱:zhongkegao@tju.edu.cn

 

主要经历:

(1) 2016.07-至今 天津大学电气自动化与信息工程学院,教授,博士生导师;

(2) 2013.07-2016.06 天津大学电气与自动化工程学院,副教授,博士生导师(2016.04)

(3) 2010.09-2013.06 天津大学电气与自动化工程学院,讲师;

(4) 2007年和2010年获天津大学工学硕士学位和博士学位;

(5) 2009年至2010年国家公派赴美国亚利桑那州立大学博士联合培养一年,美方导师为美国总统奖得主来颖诚教授;

(6) 2013年作为访问教授赴德国洪堡大学合作研究,合作者 Jürgen Kurths院士(欧洲科学院院士)

(7) 2015年作为访问教授赴香港城市大学合作研究,合作者陈关荣院士(IEEE Fellow,欧洲科学院院士)

(8) 2016年作为访问教授赴新加坡国立大学合作研究;

(9) 2018年作为访问教授赴德国PIK研究所合作研究,合作者 Jürgen Kurths院士(欧洲科学院院士)

(10) 2019年作为访问教授赴英国阿伯丁大学合作研究,合作者Celso Grebogi院士(世界科学院院士,欧洲科学院院士)

简介:

高忠科,1982年生,天津大学电气自动化与信息工程学院教授、博士生导师,国家优秀青年科学基金获得者(国家优青), 2019年全球高被引科学家,IEEE Senior Member。主要研究方向为复杂网络多源信息融合理论、新型传感器技术、多相流检测、脑机融合与混合智能等,已在IEEE Transactions on Neural Networks and Learning SystemsIEEE Transactions on Industrial InformaticsIEEE Transactions on Instrumentation and MeasurementIEEE Transactions on Systems, Man, and Cybernetics: SystemsIEEE Transactions on Circuits and Systems II: Express BriefsInternational Journal of Neural SystemsKnowledge-Based SystemsChemical Engineering Journal等国际期刊上发表SCI检索论文100余篇,其中第一/通讯作者SCI论文70余篇,论文SCI引用2100余次,Google Scholar引用3000余次,12篇第一作者论文入选ESI高被引论文。在德国Springer出版社出版英文学术专著一部,第一发明人中国发明专利32项。主持国家级项目6项,包括4项国家自然科学基金项目。获2013年全国百篇优秀博士学位论文提名奖,入选天津市131创新型人才培养工程和天津市创新人才推进计划青年科技优秀人才。2018年和20192次获得英国皇家物理学会(IOP)高被引中国作者奖。研究成果被来自美国、英国、德国、加拿大、意大利、瑞士、日本等十余个国家的数百位著名学者正面引用和评价,其中包括美国国家科学院院士,欧洲科学院院士,加拿大皇家科学院院士,美国国家工程院院士,英国皇家学会院士和多位IEEE FellowASME Fellow

 

作为负责人承担的主要科研项目:

1. 国家自然科学基金优秀青年科学基金项目,多相流传感器信息融合理论与应用,项目编号:619220622020.01-2022.12,项目负责人。

2. 国家自然科学基金面上项目,基于复杂网络和深度学习的两相流可视化与动力学建模研究,项目编号:618731812019.01-2022.12,项目负责人。

3. 国家自然科学基金面上项目,基于复杂网络多元信息融合的油井两相流流型演化机制研究,项目编号:614732032015.01-2018.12,项目负责人。

4. 国家自然科学基金青年基金项目,水平油水两相流复杂网络非线性动力学特性研究,项目编号:611041482012.01-2014.12,项目负责人。

5. 天津市自然科学基金面上项目,基于复杂网络的两相流多源异构传感器信息融合研究,项目编号:16JCYBJC182002016.04-2019.03,项目负责人。

6. 中央军委科技委项目,XXXXXX2017.07-2018.06,项目负责人。

7. 中央军委科技委项目,XXXXXX2018.10-2020.12,项目负责人。

8. 教育部高等学校博士学科点专项科研基金(新教师类),油水两相流多尺度复杂网络非线性动力学特性研究,项目编号:201100321200882012.01-2014.12,项目负责人。

9. 天津大学北洋青年学者计划人才类项目:2016.01-2019.12,项目负责人。

10. 天津大学北洋学者青年骨干教师计划人才类项目:2013.01-2014.12,项目负责人。

 

代表性论著、学术著作:

学术论文:

(1) Zhongke Gao, Xinming Wang, Yuxuan Yang, Chaoxu Mu, Qing Cai, Weidong Dang, Siyang Zuo, EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation, IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(9): 2755-2763 SCI, IF= 11.683

(2) Weidong Dang, Zhongke Gao*, Linhua Hou, Dongmei Lv, Shuming Qiu, and Guanrong Chen, A novel deep learning framework for industrial multiphase flow characterization, IEEE Transactions on Industrial Informatics, 2019, DOI: 10.1109/TII.2019.2908211SCI, IF= 7.377

(3) Zhongke Gao, Yanli Li, Yuxuan Yang, Na Dong, Xiong Yang, and Celso Grebogi, A coincidence filtering-based approach for CNNs in EEG-based recognition, IEEE Transactions on Industrial Informatics, Accepted, To be published in 2020SCI, IF= 7.377

(4) Zhongke Gao, Weidong Dang, Chaoxu Mu, Yuxuan Yang, Shan Li, Celso Grebogi, A novel multiplex network-based sensor information fusion model and its application to industrial multiphase flow system, IEEE Transactions on Industrial Informatics, 2018, 14(9): 3982-3988.SCI, IF= 7.377

(5) Yuxuan Yang, Zhongke Gao*, Yanli Li, Qing Cai, Norbert Marwan, and Juergen Kurths, A complex network-based broad learning system for detecting driver fatigue from EEG signals, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Accepted, To be published in 2020SCI, IF= 7.351

(6) Zhongke Gao, Xiaolin Hong, Weidong Dang, Linhua Hou, and Mingxu Liu, Multiresolution multiplex network for analyzing multichannel fluid flow signals, IEEE Transactions on Circuits and Systems II: Express Briefs, Accepted, To be published in 2020SCI, IF= 3.25

(7) Zhongke Gao, Shan Li, Qing Cai, Weidong Dang, Yuxuan Yang, Chaoxu Mu, Pan Hui, Relative wavelet entropy complex network for improving EEG-based fatigue driving classification, IEEE Transactions on Instrumentation and Measurement, 2019, 68(7): 2491-2497 SCI, IF= 3.067

(8) Zhongke Gao, Yuxuan Yang, Lusheng Zhai, Ningde Jin, Guanrong Chen, A four-sector conductance method for measuring and characterizing low-velocity oil-water two-phase flows, IEEE Transactions on Instrumentation and Measurement, 2016, 65(7): 1690-1697SCI, IF=  3.067

(9) Zhongke Gao, Hongtao Wang, Weidong Dang, Yongqiang Li, Xiaolin Hong, Mingxu Liu, Guanrong Chen, Complex network analysis of wire-mesh sensor measurements for characterizing vertical gas-liquid two-phase flows, IEEE Transactions on Circuits and Systems II: Express Briefs, 2020, DOI: 10.1109/TCSII.2019.2930573SCI, IF= 3.25

 

(10) Chaoxu Mu, Yong Zhang, Zhongke Gao*, Changyin Sun, ADP-based robust tracking control for a class of nonlinear systems with unmatched uncertainties, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, DOI: 10.1109/TSMC.2019.2895692SCI, IF= 7.351

(11) Qing Cai, Zhong-Ke Gao*, Yu-Xuan Yang, Wei-Dong Dang, Celso Grebogi, Multiplex limited penetrable horizontal visibility graph from EEG signals for driver fatigue detection, International Journal of Neural Systems, 2019, 29(5): 1850057SCI, IF=6.4

(12) Zhongke Gao, Yanli Li, Yuxuan Yang, Xinmin Wang, Na Dong, and Hsiao-Dong Chiang, A GPSO-optimized convolutional neural networks for EEG-based emotion recognition, Neurocomputing, 2020, DOI: 10.1016/j.neucom.2019.10.096SCI, IF= 4.072

(13) Na Dong, Jian-Fang Chang, Ai-Guo Wu, Zhong-Ke Gao*, A novel convolutional neural network framework based solar irradiance prediction method, International Journal of Electrical Power & Energy Systems, 2020, DOI: 10.1016/j.ijepes.2019.105411SCI, IF= 4.418

 

(14) Na Dong, Yingjie Li, Zhongke Gao*, Wai Hung Ip, Kai Leung Yung, A WPCA-based method for detecting fatigue driving from EEG-based internet of vehicles system, IEEE Access, 2019, 7: 124702-124711SCI, IF= 4.098 

 

(15) Zhongke Gao, Kaili Zhang, Weidong Dang, Yuxuan Yang, Zibo Wang, Haibin Duan, Guanrong Chen, An adaptive optimal-Kernel time-frequency representation-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system, Knowledge-Based Systems, 2018, 152: 163-171.SCI, IF=5.101

(16) Wei-Dong Dang, Zhong-Ke Gao*, Domg-Mei Lv, Ming-Xu Liu, Qing Cai, Xiao-Lin Hong, A novel time-frequency multilayer network for multivariate time series analysis, New Journal of Physics, 2018, 20: 125005SCI, IF=3.773

(17) Zhongke Gao, Zibo Wang, Chao Ma, Weidong Dang, Kaili Zhang, A wavelet time-frequency representation based complex network method for characterizing brain activities underlying motor imagery signals , IEEE Access, 2018, 6: 65796-65802SCI, IF= 4.098

(18) Zhong-Ke Gao, Yu-Xuan Yang, Lu-Sheng Zhai, Mei-Shuang Ding, Ning-De Jin, Characterizing slug to churn flow transition by using multivariate pseudo Wigner distribution and multivariate multiscale entropy, Chemical Engineering Journal, 2016291:74-81 SCI, IF=8.355

(19) Lian-Xin Zhuang, Ning-De Jin, An Zhao, Zhong-Ke Gao*, Lu-Sheng Zhai, Yi Tang, Nonlinear multi-scale dynamic stability of oil-gas-water three-phase flow in vertical upward pipe, Chemical Engineering Journal, 2016, 302:595-608SCI, IF=8.355

(20) Zhong-Ke Gao, Peng-Cheng Fang, Mei-Shuang Ding, Ning-De Jin, Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow, Experimental Thermal and Fluid Science, 2015, 60: 157-164SCI, IF=3.493

(21) Zhong-Ke Gao, Qing Cai, Yu-Xuan Yang, Wei-Dong Dang, Shan-Shan Zhang, Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series, Scientific Reports, 2016, 6: 35622SCI, IF=5.228

(22) Zhong-Ke Gao, Michael Small, Jürgen Kurths, Complex network analysis of time series, Europhysics Letters, 2016, 116: 50001SCI, IF=2.269

(23) Zhong-Ke Gao, Yu-Xuan Yang, et al., Multiscale complex network for analyzing experimental multivariate time series, Europhysics Letters, 2015, 109: 30005SCI, IF=2.269

(24) Zhong-Ke Gao, Yu-Xuan Yang, et al., Multi-frequency complex network from time series for uncovering oil-water flow structure, Scientific Reports, 2015, 5: 8222SCI, IF=5.228

(25) Zhong-Ke Gao and Ning-De Jin, A directed weighted complex network for characterizing chaotic dynamics from time series, Nonlinear Analysis-Real World Applications, 2012, 13(2): 947-952SCI, IF=2.519

(26) Zhong-Ke Gao, Wei-Dong Dang, Yu-Xuan Yang, Qing Cai, Multiplex recurrence network from multi-channel signals for revealing oil-water spatial flow behavior, Chaos, 2017, 27: 035809SCI, IF=2.643

(27) Yu-Xuan Yang, Zhong-Ke Gao*, Xin-Min Wang, Yan-Li Li, Jing-Wei Han, Norbert Marwan, Juergen Kurths, A recurrence quantification analysis-based channel-frequency convolutional neural network for emotion recognition from EEG, Chaos, 2018, 28: 085724 (SCI, IF=2.643)

(28) Zhong-Ke Gao, Shan Li, Wei-Dong Dang, Yu-Xuan Yang, Younghae Do, Celso Grebogi, Wavelet Multiresolution Complex Network for Analyzing Multivariate Nonlinear Time Series, International Journal of Bifurcation and Chaos, 2017, 27(8): 1750123SCI, IF=2.145

(29) Zhong-Ke Gao, Qing Cai, Yu-Xuan Yang, Na Dong, Shan-Shan Zhang, Visibility graph from adaptive optimal kernel time-frequency representation for classification of epileptiform EEG, International Journal of Neural Systems, 2017, 27(4):1750005SCI, IF=6.4

(30) Zhong-Ke Gao, Zi-Bo Wang, Yu-Xuan Yang, Shan Li, Wei-Dong Dang, Xiao-Qian Mao, Wavelet multiresolution complex network for decoding brain fatigued behavior from P300 signals, Physica A, 2018, 506: 221-228SCI, IF=2.5

(31) Zhong-Ke Gao, Shan-Shan Zhang, Wei-Dong Dang, Shan Li, Qing Cai, Multilayer network from multivariate time series for characterizing nonlinear flow behavior, International Journal of Bifurcation and Chaos, 2017, 27(4): 1750059SCI, IF=2.145

(32) Zhong-Ke Gao, Xin-Wang Zhang, Ning-De Jin, Reik V. Donner, Norbert Marwan, Jürgen Kurths, Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows, Europhysics Letters, 2013,103:50004SCI, IF=2.269

(33) Zhong-Ke Gao, Ning-De Jin, Nonlinear characterization of oil-gas-water three-phase flow in complex networks, Chemical Engineering Science, 2011, 66(12): 2660-2671SCI, IF=3.372

(34) Zhong-Ke Gao, Xin-Wang Zhang, Ning-De Jin, Norbert Marwan, Jürgen Kurths, Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow, Physical Review E, 2013, 88(3): 032910SCI, IF=2.353

(35) Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang, Ying-Cheng Lai, Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic features, Physical Review E, 2010, 82(2): 016210SCI, IF=2.353

 

学术论著:

在德国斯普林格(Springer)出版社出版英文学术专著一部《Nonlinear Analysis of Gas-Water/Oil-water Two-Phase Flow in Complex Networks》,出版日期:20141月。

英文学术专著:

Zhong-Ke Gao, Ning-De Jin, Wen-Xu Wang, Nonlinear Analysis of Gas-Water/ Oil-water Two-Phase Flow in Complex Networks, Springer, Berlin, Germany, 2014, ISBN: 978-3-642-38372-4.

 

国家发明专利:

(1)      一种用于两相流检测的四扇区分布式电导传感器,发明专利,第一发明人,专利号:2014100333387(已授权)

(2)      一种分布式电导传感器的结构参数优化方法,发明专利,第一发明人,专利号:2014100333372(已授权)

(3)      一种基于分布式电导传感器的两相流测量系统,发明专利,第一发明人,专利号:2014100339415(已授权)

(4)      基于模态迁移复杂网络的气液相含率测量及验证方法,发明专利,第一发明人,专利号:2014102291181(已授权)

(5)      基于频率复杂网络的垂直油水相含率测量及验证方法,发明专利,第一发明人,专利号:2014102287186(已授权)

(6)      基于多元相空间复杂网络的油水相含率测量及验证方法,发明专利,第一发明人,专利号:2014102287190(已授权)

(7)      基于多层复杂网络的两相流多元复阻抗检测信息融合方法,发明专利,第一发明人,专利号:2016108891696(已授权)

(8)      基于复杂网络的深度学习模型及在测量信号分析中的应用,发明专利,第一发明人,专利号:2016108881247(已授权)  

(9)      基于网格传感器的两相流空间复杂网络可视化分析方法,发明专利,第一发明人,专利号:2016108876817(已授权)

(10)   基于递归图的深度学习模型及在油水相含率测量中的应用,发明专利,第一发明人,专利号:2016108884902(已授权)

(11)   基于多尺度加权递归网络的两相流网络可视化方法及应用,发明专利,第一发明人,专利号:2016108891709(已授权)

(12)   基于复杂网络和深度学习的两相流多元信息融合法及应用,发明专利,第一发明人,专利号:2016108893579(已授权)

(13)   基于小波多分辨率双层复杂网络的多源信息融合法及应用,发明专利,第一发明人,专利号:2016108886166(已授权)

(14)   用于脑状态监测的头戴式智能穿戴电极数量优化法及应用,发明专利,第一发明人,专利号:2016108876840(已授权)

(15)   基于多尺度网络的深度学习模型及在脑状态监测中的应用,发明专利,第一发明人,专利号:2016108876836(已授权)

(16)   基于复杂网络的脑电信号分析方法及应用,发明专利,第一发明人,专利号:2016108891681(已授权)

(17)   基于最优核时频分布可视图的癫痫脑电信号识别方法,发明专利,第一发明人,专利号:2016108876821(已授权)

(18)   基于复杂网络的心电信号分析方法及在智能穿戴上的应用,发明专利,第一发明人,专利号:2016108886170(已授权)

(19)   基于脑电波分析的便携式意念拨号系统,发明专利,第一发明人,专利号:2018101695769(已授权) 

 

主要学术成就、奖励及荣誉:

(1) 2019年国家优青

(2) 2019年全球高被引科学家

(3) 2019年英国皇家物理学会(IOP)高被引中国作者奖

(4) 2018年英国皇家物理学会(IOP)高被引中国作者奖

(5) 2017年入选天津市创新人才推进计划青年科技优秀人才

(6) 2013年全国百篇优秀博士学位论文提名奖

(7) 2013年入选天津市131创新型人才培养工程 (第二层次人才)

 

其他(社会兼职等):

(1)  IEEE Senior Member

(2) 中国自动化学会高级会员

(3) 国家核电核岛装备产业计量测试联盟副理事长

(4) 中国指挥与控制学会网络科学与工程专委会委员

(5) 中国工业与应用数学学会复杂网络与复杂系统专委会委员

(6) 中国自动化学会能源互联网专委会委员

(7) 中国自动化学会环境感知与保护自动化专委会委员

(8) 中国自动化学会青年工作委员会委员

 

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